gemini_model

.gradio/certificate.pem

@@ -1,31 +0,0 @@
------BEGIN CERTIFICATE-----
-MIIFazCCA1OgAwIBAgIRAIIQz7DSQONZRGPgu2OCiwAwDQYJKoZIhvcNAQELBQAw
-TzELMAkGA1UEBhMCVVMxKTAnBgNVBAoTIEludGVybmV0IFNlY3VyaXR5IFJlc2Vh
-cmNoIEdyb3VwMRUwEwYDVQQDEwxJU1JHIFJvb3QgWDEwHhcNMTUwNjA0MTEwNDM4
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-rU7m2Ys6xt0nUW7/vGT1M0NPAgMBAAGjQjBAMA4GA1UdDwEB/wQEAwIBBjAPBgNV
-HRMBAf8EBTADAQH/MB0GA1UdDgQWBBR5tFnme7bl5AFzgAiIyBpY9umbbjANBgkq
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-4RgqsahDYVvTH9w7jXbyLeiNdd8XM2w9U/t7y0Ff/9yi0GE44Za4rF2LN9d11TPA
-mRGunUHBcnWEvgJBQl9nJEiU0Zsnvgc/ubhPgXRR4Xq37Z0j4r7g1SgEEzwxA57d
-emyPxgcYxn/eR44/KJ4EBs+lVDR3veyJm+kXQ99b21/+jh5Xos1AnX5iItreGCc=
------END CERTIFICATE-----

agent.py

@@ -1,117 +0,0 @@
-import os
-from pathlib import Path
-from typing import Optional, Union
-
-import pandas as pd
-from dotenv import load_dotenv
-from smolagents import (CodeAgent, DuckDuckGoSearchTool, FinalAnswerTool,
-                        LiteLLMModel, PythonInterpreterTool,
-                        WikipediaSearchTool)
-from smolagents.tools import Tool
-from tabulate import tabulate
-
-# Load environment variables
-load_dotenv()
-
-# Initialize the model
-model = LiteLLMModel(
-    model_id=os.getenv("GEMINI_MODEL"), api_key=os.getenv("GEMINI_API_KEY")
-)
-
-
-class ExcelToTextTool(Tool):
-    """Render an Excel worksheet as a Markdown table."""
-
-    name = "excel_to_text"
-    description = (
-        "Read an Excel file and return a Markdown table of the requested sheet. "
-        "Accepts either the sheet name or a zero-based index (as a string)."
-    )
-
-    inputs = {
-        "excel_path": {
-            "type": "string",
-            "description": "Path to the Excel file (.xlsx or .xls).",
-        },
-        "sheet_name": {
-            "type": "string",
-            "description": (
-                "Worksheet name or zero-based index (as a string). "
-                "Optional; defaults to the first sheet."
-            ),
-            "nullable": True,
-        },
-    }
-
-    output_type = "string"
-
-    def forward(self, excel_path: str, sheet_name: Optional[str] = None) -> str:
-        """Load the Excel file and return the sheet as a Markdown table.
-
-        Args:
-            excel_path: Path to the Excel file.
-            sheet_name: Optional name or index of the sheet to read. If None, reads the first sheet.
-
-        Returns:
-            A Markdown table representing the Excel sheet, or an error message if the file is not found or cannot be read.
-        """
-
-        file_path = Path(excel_path).expanduser().resolve()
-        if not file_path.is_file():
-            return f"Error: Excel file not found at {file_path}"
-
-        try:
-            sheet: Union[str, int] = (
-                int(sheet_name)
-                if sheet_name and sheet_name.isdigit()
-                else sheet_name or 0
-            )
-
-            df = pd.read_excel(file_path, sheet_name=sheet)
-
-            if hasattr(df, "to_markdown"):
-                return df.to_markdown(index=False)
-
-            return tabulate(df, headers="keys", tablefmt="github", showindex=False)
-
-        except Exception as e:
-            return f"Error reading Excel file: {e}"
-
-
-class GaiaAgent:
-    """An agent capable of using tools to answer general questions."""
-
-    def __init__(self):
-        """Initializes the GaiaAgent with a set of tools."""
-
-        print("GaiaAgent initialized with tools.")
-
-        tools = [
-            DuckDuckGoSearchTool(),
-            WikipediaSearchTool(),
-            ExcelToTextTool(),
-            PythonInterpreterTool(),
-            FinalAnswerTool(),
-        ]
-
-        self.agent = CodeAgent(
-            model=model,
-            tools=tools,
-            add_base_tools=True,
-            additional_authorized_imports=["pandas", "numpy", "csv", "subprocess"],
-        )
-
-    def __call__(self, task_id: str, question: str) -> str:
-        """Processes a question using the agent and its tools.
-
-        Args:
-            task_id: A unique identifier for the task.
-            question: The question to be answered.
-
-        Returns:
-            The answer generated by the agent.
-        """
-        print(f"Agent received task_id='{task_id}' | question='{question[:50]}...'")
-        answer = self.agent.run(question)
-        print(f"Agent returning answer: {answer}")
-        return answer

app.py

@@ -1,6 +1,6 @@
 import os
 
-import agent
+import gaia_agent
 import gradio as gr
 import logic
 import pandas as pd
@@ -9,9 +9,7 @@ from dotenv import load_dotenv
 load_dotenv()
 
 
-def run_and_submit_all(
-    profile: gr.OAuthProfile | None,
-) -> tuple[str, pd.DataFrame | None]:
+def run_and_submit_all(profile: gr.OAuthProfile | None):
     """Fetches all questions, runs the BasicAgent on them, submits all answers,
     and displays the results.
 
@@ -41,7 +39,7 @@ def run_and_submit_all(
 
     # 1. Instantiate Agent
     try:
-        gaia_agent = agent.GaiaAgent()
+        agent = gaia_agent.GaiaAgent()
     except Exception as e:
         print(f"Error instantiating agent: {e}")
         return f"Error initializing agent: {e}", None
@@ -53,7 +51,7 @@ def run_and_submit_all(
         return str(e), None
 
     # 3. Run the Agent
-    results_log, answers_payload = logic.run_agent(gaia_agent, questions_data)
+    results_log, answers_payload = logic.run_agent(agent, questions_data)
     if not answers_payload:
         print("Agent did not produce any answers to submit.")
         return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
@@ -138,4 +136,4 @@ if __name__ == "__main__":
     print("-" * (60 + len(" App Starting ")) + "\n")
 
     print("Launching Gradio Interface for Basic Agent Evaluation...")
-    gaia_ui.launch(debug=True, share=True)
+    gaia_ui.launch(debug=True, share=False)

gaia_agent.py

@@ -0,0 +1,33 @@
+class GaiaAgent:
+    """
+    A basic agent that receives a question and returns a fixed answer.
+
+    This class serves as a placeholder or a simple baseline agent for testing
+    and demonstration purposes. It does not perform any sophisticated
+    reasoning or information retrieval.
+    """
+
+    def __init__(self):
+        """
+        Initializes the GaiaAgent.
+
+        Currently, this constructor simply prints a message to the console.
+        In a more complex implementation, this method might load a model,
+        connect to a database, or perform other setup tasks.
+        """
+        print("BasicAgent initialized.")
+
+    def __call__(self, question: str) -> str:
+        """
+        Processes a question and returns a fixed answer.
+
+        Args:
+            question: The question to be processed.
+
+        Returns:
+            A fixed string representing the agent's answer.
+        """
+        print(f"Agent received question (first 50 chars): {question[:50]}...")
+        fixed_answer = "This is a default answer."
+        print(f"Agent returning fixed answer: {fixed_answer}")
+        return fixed_answer

logic.py

@@ -1,17 +1,14 @@
 from typing import Dict, List, Tuple
-import re
-import tempfile
-from pathlib import Path
+
 import pandas as pd
 import requests
-from agent import GaiaAgent
+from gaia_agent import GaiaAgent
 from pandas import DataFrame
 
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 QUESTIONS_URL = f"{DEFAULT_API_URL}/questions"
 SUBMIT_URL = f"{DEFAULT_API_URL}/submit"
-FILE_PATH = f"{DEFAULT_API_URL}/files/"
 
 
 # --- Helper Methods ---
@@ -116,9 +113,8 @@ def submit_answers(submission_data: dict, results_log: list) -> Tuple[str, DataF
         return status_message, results_df
 
 
-def run_agent(
-    gaia_agent: GaiaAgent, questions_data: List[Dict]
-) -> Tuple[List[Dict], List[Dict]]:
+def run_agent(agent: GaiaAgent, 
+              questions_data: List[Dict]) -> Tuple[List[Dict], List[Dict]]:
     """Runs the agent on a list of questions and returns the results and answers.
 
     This function iterates through a list of questions, runs the provided agent on each
@@ -126,7 +122,7 @@ def run_agent(
     agent execution and returns the results log and the answers payload.
 
     Args:
-        gaia_agent (GaiaAgent): An instance of the GaiaAgent class, which is responsible for
+        agent (GaiaAgent): An instance of the GaiaAgent class, which is responsible for
             generating answers to the questions.
         questions_data (List[Dict]): A list of dictionaries, where each dictionary
             represents a question and contains at least the 'task_id' and 'question' keys.
@@ -145,12 +141,11 @@ def run_agent(
     for item in questions_data:
         task_id = item.get("task_id")
         question_text = item.get("question")
-        question_text = process_file(task_id, question_text)
         if not task_id or question_text is None:
             print(f"⚠️ Skipping invalid item (missing task_id or question): {item}")
             continue
         try:
-            submitted_answer = gaia_agent(task_id, question_text)
+            submitted_answer = agent(question_text)
             answers_payload.append(
                 {"task_id": task_id, "submitted_answer": submitted_answer}
             )
@@ -166,42 +161,3 @@ def run_agent(
             }
         )
     return results_log, answers_payload
-
-
-def process_file(task_id: str, question_text: str) -> str:
-    """
-    Attempt to download a file associated with a task from the API.
-
-    - If the file exists (HTTP 200), it is saved to a temp directory and the local file path is returned.
-    - If no file is found (HTTP 404), returns None.
-    - For all other HTTP errors, the exception is propagated to the caller.
-    """
-    file_url = f"{FILE_PATH}{task_id}"
-
-    try:
-        response = requests.get(file_url, timeout=30)
-        response.raise_for_status()
-    except requests.exceptions.RequestException as exc:
-        print(f"Exception in download_file>> {str(exc)}")
-        return question_text # Unable to get the file
-
-    # Determine filename from 'Content-Disposition' header, fallback to task_id
-    content_disposition = response.headers.get("content-disposition", "")
-    filename = task_id
-    match = re.search(r'filename="([^"]+)"', content_disposition)
-    if match:
-        filename = match.group(1)
-
-    # Save file in a temp directory
-    temp_storage_dir = Path(tempfile.gettempdir()) / "gaia_cached_files"
-    temp_storage_dir.mkdir(parents=True, exist_ok=True)
-
-    file_path = temp_storage_dir / filename
-    file_path.write_bytes(response.content)
-    return (
-                f"{question_text}\n\n"
-                f"---\n"
-                f"A file was downloaded for this task and saved locally at:\n"
-                f"{str(file_path)}\n"
-                f"---\n\n"
-            )

requirements.txt

@@ -2,9 +2,4 @@ gradio
 gradio[oauth]
 requests
 python-dotenv
-pandas
-smolagents
-wikipedia-api
-google-generativeai
-smolagents[litellm]
-tabulate
+pandas